How Does the Methodology of 3D Structure Preparation Influence the Quality of pK_a Prediction?

Abstract

The acid dissociation constant is an important molecular property and it can be successfully predicted by Quantitative Structure-Property Relationship (QSPR) models, even for in silico designed molecules. We analyzed how the methodology of in silico 3D structure preparation influences the quality of QSPR models. Specifically, we evaluated and compared QSPR models based on six different 3D structure sources (DTP NCI, Pubchem, Balloon, Frog2, OpenBabel and RDKit) combined with four different types of optimization. These analyses were performed for three classes of molecules (phenols, carboxylic acids, anilines) and the QSPR model descriptors were quantum mechanical (QM) and empirical partial atomic charges. Specifically, we developed 516 QSPR models and afterwards systematically analyzed the influence of the 3D structure source and other factors on their quality. Our results confirmed that QSPR models based on partial atomic charges are able to predict pKa with high accuracy. We also confirmed that ab-initio and semiempirical QM charges provide very accurate QSPR models, and using empirical charges based on electronegativity equalization is also acceptable, as well as advantageous, since their calculation is very fast. On the other hand, Gasteiger-Marsili empirical charges are not applicable for pKa prediction. We later found that QSPR models for some classes of molecules (carboxylic acids) are less accurate. In this context, we compared the influence of different 3D structure sources. We found that an appropriate selection of 3D structure source and optimization method is essential for the successful QSPR modeling of pKa. Specifically, the 3D structures from the DTP NCI and Pubchem databases performed the best, as they provided very accurate QSPR models for all the tested molecular classes and charge calculation approaches, and they do not require optimization. Also Frog2 performed very well. Other 3D structure sources can also be used, but are not so robust, and an unfortunate combination of molecular class and charge calculation approach can produce weak QSPR models. Additionally, these 3D structures generally need optimization in order to produce good quality QSPR models.

AtomicChargeCalculator: interactive web-based calculation of atomic charges in large biomolecular complexes and drug-like molecules

“…We then wondered if this accuracy, though unable to reflect suitable changes depending on the nature or position of the substituents, was sufficient to build acceptable QSPR models for prediction. No suitable QSPR models are available for benzoic acids, but such models for aliphatic carboxylic acids have been reported [ 58 , 78 ]. The descriptors used by these QSPR models consist of charges on the atoms of the carboxylic group in both the neutral ( , , , ) and dissociated forms ( , , ).…”

High-quality and universal empirical atomic charges for chemoinformatics applications

“…These approaches can be divided into conformationally-independent, which are based on 2D structure (e.g., Gasteiger’s and Marsili’s PEOE [ 40 , 41 ], GDAC [ 42 ], KCM [ 43 ], DENR [ 44 ]) and conformationally-dependent, calculated from 3D structure (e.g., EEM [ 45 ], QEq [ 46 ] or SQE [ 47 , 48 ]). We would like to highlight that conformationally-dependent charges are considered to be more suitable for chemoinformatics applications [ 1 – 3 , 7 , 12 , 20 ]. The reason is that these charges contain extensive information not only about chemical surrounding of atoms, i.e., its topology (2D structure based charges) but also geometry and “chemical quality” of the surrounding.…”

BioShell 3.0: Library for Processing Structural Biology Data

“…They implemented their methodology as BioShell scripts and concluded that "BioShell combined with the methodology presented in this paper, is crucial in order to predict protein structures while avoiding structural clashes". In yet another work done by Abagyan [13] group, BioShell was used to reconstruct atoms and larger parts of chemical groups missing in protein structures. In this contribution, we present the newest version, rewritten in C++11, which provides widely extended functionality.…”